Problems in multi-sensor data fusion are addressed for passive (angle-only) sensors; the example used is a constellation of IR sensors on satellites in low-earth orbit, viewing up to several hundred ballistic missile targets. The sensor data used in the methodology of the report is 'post-detection,' with targets resolved on single pixels (it is possible for several targets to be resolved on the same pixel). A 'scan' by a sensor is modeled by the formation of a rectangular focal plane image of lit pixels (bits with value 1), representing the presence of at least one target, and unlit pixels (bits with value 0), representing the absence of a target, at a particular time. Approaches and algorithmic solutions are developed which address the following passive sensor data fusion problems: scan-to-scan target association, and association classification. The ultimate objective is to estimate target states, for use in a larger battle management system. Results indicate that successful scan-to-scan target association is feasible at scan rates >=2 Hz, independent of resolution. Sensor-to-sensor target association is difficult at low resolution; even with high-resolution sensors the performance of a standard two-sensor single scan approach is variable and unpredictable, since it is a function of the relative geometry of sensors and targets. A single-scan approach using the Varad algorithm and three sensors is not as sensitive to this relative geometry, but is usable only for high-resolution sensors. Innovative multi-scan and multi-sensor modifications of the three- sensor Varad algorithm are developed which provide excellent performance for a wide range of sensor resolutions. The multi-sensor multi-scan methodology also provides accurate information on the classification of target associations as correct or incorrect. For the scenarios examined with resolution cell sizes ranging from 300 m to 2 km, association errors are less than 5% and essentially no classification errors are made, when sensor data is integrated over a 60 s time period. With higher-resolution sensors, better results are achievable in less time. The results of the data fusion from three or more sensors over such a period of time provide a rich source of information for the estimation of target states. The algorithms are fast (O(n ln n)); for approximately 100 targets, the average processing per scan in the multi-scan three-sensor methodology takes approximately a second of computational time on a Mac II.